Dataflow life cycles

ABSTRACT

System and methods for implementing dataflow life cycles are described and include forming, by a first server computing system, a dataflow life cycle by associating a dataflow with a customized code; associating, by the first server computing system, the customized code of the dataflow life cycle with context information, the customized code including one or more of pre-processing customized code and post-processing customized code; scheduling, by the first server computing system, the dataflow of the dataflow life cycle to be executed by a second server computing system when the customized code includes the pre-processing customized code and when the pre-processing customized code is successfully executed by the first server computing system; and executing, by the first server computing system, the post-processing customized code when the customized code includes the post-processing customized code and when the dataflow of the dataflow life cycle is successfully executed by the second server computing system.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever.

TECHNICAL FIELD

The present disclosure relates generally to data processing and more specifically relates to pre-processing and post-processing customized codes associated with dataflows.

BACKGROUND

The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also be inventions.

In general, an analytic dataflow framework may be used to define and execute dataflows to generate data that may make it easier for business users to use for analytic purposes. One example of such analytic dataflow framework is the Wave Analytics platform by Salesforce.com of San Francisco, Calif. Wave Analytics is a cloud-based platform for connecting data from multiple sources, creating interactive views of that data, and sharing those views in dashboards. Wave Analytics enables distributing insight to business users so they can understand and take action on changing information.

A dataflow may include a set of instructions that specifies what data to extract from objects, load the data, index the data, and make the data available for analysis or additional transformations. This may be referred to as an Extract, Load and Transform (ELT) process. The process of causing a dataflow to be executed is shown in an example in FIG. 1A. A dataflow 106 may be set up by a user (e.g., a business user) using a dataflow user interface (UI) associated with the application server 105. Once set up, execution of the dataflow 106 may be requested by a scheduler (also referred to as an ELT scheduler) 107 in the application server 105. The dataflow 106 may be submitted to a processing service 110. The processing service 110 may execute the dataflow 106 using objects stored in the database 112. The dataflow 106 may not be associated with any context information. For example, it may be unknown know who creates the dataflow 106, what the dataflow 106 is for, etc.

When the execution of the dataflow 106 is completed, a transformed dataset 115 may be generated. The processing service 110 may notify the scheduler 107 of the completion of the execution of the dataflow 106. A user may monitor the execution status of the dataflow using the dataflow UI. The submission of the dataflow 106 by the scheduler 107 and the receipt of the notification of the execution completion of the dataflow 106 by the scheduler 107 may be asynchronous. As far as the scheduler 107 is concerned, submitting the dataflow 106 to the processing service 110 is a send and forget operation. With increasing adoption of the ELT process, there is a need to incorporate customized operations and context information to dataflows in the existing dataflow framework.

BRIEF SUMMARY

For some embodiments, systems and methods for implementing dataflow life cycles may include forming, by a first server computing system, a dataflow life cycle by associating a dataflow with a customized code; associating, by the first server computing system, the customized code of the dataflow life cycle with context information, the customized code including one or more of pre-processing customized code and post-processing customized code; scheduling, by the first server computing system, the dataflow of the dataflow life cycle to be executed by a second server computing system when the customized code includes the pre-processing customized code and when the pre-processing customized code is successfully executed by the first server computing system; and executing, by the first server computing system, the post-processing customized code when the customized code includes the post-processing customized code and when the dataflow of the dataflow life cycle is successfully executed by the second server computing system. Other aspects and advantages of the present invention can be seen on review of the drawings, the detailed description and the claims, which follow.

BRIEF DESCRIPTION OF THE DRAWINGS

The included drawings are for illustrative purposes and serve only to provide examples of possible structures and process steps for the disclosed techniques. These drawings in no way limit any changes in form and detail that may be made to embodiments by one skilled in the art without departing from the spirit and scope of the disclosure.

FIG. 1A shows a diagram of an example execution of a dataflow based on a dataflow framework that may be used with some embodiments.

FIG. 1B shows a diagram of an example computing system that may be used with some embodiments.

FIG. 2 shows a diagram of an example network environment that may be used with some embodiments.

FIGS. 3A and 3B shows diagrams of an example of a dataflow life cycle including configuration information and version information, in accordance with some embodiments.

FIG. 4 shows a diagram of an example dataflow that is incorporated with pre-processing customized codes, in accordance with some embodiments.

FIG. 5 shows a diagram of an example dataflow that is incorporated with post-processing customized codes, in accordance with some embodiments.

FIG. 6 is an example flow chart that shows execution of pre-processing customized codes prior to execution of a dataflow, in accordance with some embodiments.

FIG. 7 is an example flowchart that shows execution of post-processing customized codes after execution of a dataflow, in accordance with some embodiments.

FIG. 8A shows a system diagram illustrating architectural components of an applicable environment, in accordance with some embodiments.

FIG. 8B shows a system diagram further illustrating architectural components of an applicable environment, in accordance with some embodiments.

FIG. 9 shows a system diagram illustrating the architecture of a multi-tenant database environment, in accordance with some embodiments.

FIG. 10 shows a system diagram further illustrating the architecture of a multi-tenant database environment, in accordance with some embodiments.

DETAILED DESCRIPTION

Systems and methods for associating dataflows with one or more of pre-processing customized codes and post-processing customized codes to form dataflow life cycles are disclosed. One or more of pre-processing codes and post-processing codes may be associated with a dataflow using connectors. Execution of a dataflow life cycle may include execution of the pre-processing customized code by a first server computing system, executing of the dataflow by a second server computing system, and execution of the post-processing customized code by the first server computing system. The execution of the pre-processing customized code may include generating data needed for the execution of the dataflow. The execution of the post-processing customized code may include issuing notification. Context information associated with the pre-processing and post-processing customized codes may be used for notification.

In general, dataflow execution may be used to extract, load and transform (ELT) data, regardless where the dataflow is executed, Dataflow execution may involve a system pushing the data massaged by pre-processing operations through a directed acyclic graph (DAG) graph having multiple nodes with each node being an operator. A dataflow may be executed on spark as well, but it still goes through a DAG of operator nodes. An operator may be an operation to extract data from one or more sources, flatten the data, filter or join the data with another data etc. Dataflow execution may be performed by a processing system such as for example, a system implemented with the Superpod product of Salesforce.com. The Superpod may help providing big organizations a dedicated instance of an application service (e.g., Salesforce.com application services).

The systems and methods associated with dataflow life cycles will be described with reference to example embodiments. These examples are being provided solely to add context and aid in the understanding of the present disclosure. It will thus be apparent to one skilled in the art that the techniques described herein may be practiced without some or all of these specific details. In other instances, well known process steps have not been described in detail in order to avoid unnecessarily obscuring the present disclosure. Other applications are possible, such that the following examples should not be taken as definitive or limiting either in scope or setting.

In the following detailed description, references are made to the accompanying drawings, which form a part of the description and in which are shown, by way of illustration, specific embodiments. Although these embodiments are described in sufficient detail to enable one skilled in the art to practice the disclosure, it is understood that these examples are not limiting, such that other embodiments may be used and changes may be made without departing from the spirit and scope of the disclosure.

As used herein, the term “multi-tenant database system” refers to those systems in which various elements of hardware and software of the database system may be shared by one or more customers. For example, a given application server may simultaneously process requests for a great number of customers, and a given database table may store rows for a potentially much greater number of customers.

The described subject matter may be implemented in the context of any computer-implemented system, such as a software-based system, a database system, a multi-tenant environment, or the like. Moreover, the described subject matter may be implemented in connection with two or more separate and distinct computer-implemented systems that cooperate and communicate with one another. One or more embodiments may be implemented in numerous ways, including as a process, an apparatus, a system, a device, a method, a computer readable medium such as a computer readable storage medium containing computer readable instructions or computer program code, or as a computer program product comprising a computer usable medium having a computer readable program code embodied therein.

The disclosed embodiments may include systems and methods for implementing dataflow life cycles. The method includes forming, by a first server computing system, a dataflow life cycle by associating a dataflow with a customized code; associating, by the first server computing system, the customized code of the dataflow life cycle with context information, the customized code including one or more of pre-processing customized code and post-processing customized code; scheduling, by the first server computing system, the dataflow of the dataflow life cycle to be executed by a second server computing system when the customized code includes the pre-processing customized code and when the pre-processing customized code is successfully executed by the first server computing system; and executing, by the first server computing system, the post-processing customized code when the customized code includes the post-processing customized code and when the dataflow of the dataflow life cycle is successfully executed by the second server computing system.

The disclosed embodiments may include a system for implementing dataflow life cycles and include one or more processors; and a non-transitory computer readable medium storing a plurality of instructions, which when executed, cause the one or more processors of a first server computing to form a dataflow life cycle by associating a dataflow with a customized code; associate the customized code of the dataflow life cycle with context information, the customized code including one or more of pre-processing customized code and post-processing customized code; schedule the dataflow of the dataflow life cycle to be executed by a second server computing system when the customized code includes the pre-processing customized code and when the pre-processing customized code is successfully executed by the first server computing system; and execute the post-processing customized code when the customized code includes the post-processing customized code and when the dataflow of the dataflow life cycle is successfully executed by the second server computing system.

The disclosed embodiments may include a computer program product comprising computer-readable program code to be executed by one or more processors of a first server computing system when retrieved from a non-transitory computer-readable medium, the program code including instructions to form a dataflow life cycle by associating a dataflow with a customized code; associate the customized code of the dataflow life cycle with context information, the customized code including one or more of pre-processing customized code and post-processing customized code; schedule the dataflow of the dataflow life cycle to be executed by a second server computing system when the customized code includes the pre-processing customized code and when the pre-processing customized code is successfully executed by the first server computing system; and execute the post-processing customized code when the customized code includes the post-processing customized code and when the dataflow of the dataflow life cycle is successfully executed by the second server computing system.

While one or more implementations and techniques are described with reference to an embodiment relating to a dataflow life cycle implemented in a system having an application server providing a front end for an on-demand database service capable of supporting multiple tenants, the one or more implementations and techniques are not limited to multi-tenant databases nor deployment on application servers. Embodiments may be practiced using other database architectures, i.e., ORACLE®, DB2® by IBM and the like without departing from the scope of the embodiments claimed.

Any of the above embodiments may be used alone or together with one another in any combination. The one or more implementations encompassed within this specification may also include embodiments that are only partially mentioned or alluded to or are not mentioned or alluded to at all in this brief summary or in the abstract. Although various embodiments may have been motivated by various deficiencies with the prior art, which may be discussed or alluded to in one or more places in the specification, the embodiments do not necessarily address any of these deficiencies. In other words, different embodiments may address different deficiencies that may be discussed in the specification. Some embodiments may only partially address some deficiencies or just one deficiency that may be discussed in the specification, and some embodiments may not address any of these deficiencies.

The described subject matter may be implemented in the context of any computer-implemented system, such as a software-based system, a database system, a multi-tenant environment, or the like. Moreover, the described subject matter may be implemented in connection with two or more separate and distinct computer-implemented systems that cooperate and communicate with one another. One or more implementations may be implemented in numerous ways, including as a process, an apparatus, a system, a device, a method, a computer readable medium such as a computer readable storage medium containing computer readable instructions or computer program code, or as a computer program product comprising a computer usable medium having a computer readable program code embodied therein.

FIG. 1B is a diagram of an example computing system that may be used with some embodiments of the present invention. The computing system 102 may be used by a user to log into a server computing system to define a dataflow, associate the dataflow with one or more of pre-processing customized codes and post-processing customized codes. For example, the user may be a developer. For some embodiments, the server computing system may be associated with a multi-tenant database environment. For example, the multi-tenant database environment may be associated with the services provided by Salesforce.com®.

The computing system 102 is only one example of a suitable computing system, such as a mobile computing system, and is not intended to suggest any limitation as to the scope of use or functionality of the design. Neither should the computing system 102 be interpreted as having any dependency or requirement relating to any one or combination of components illustrated. The design is operational with numerous other general purpose or special purpose computing systems. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with the design include, but are not limited to, personal computers, server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, mini-computers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. For example, the computing system 102 may be implemented as a mobile computing system such as one that is configured to run with an operating system (e.g., iOS) developed by Apple Inc. of Cupertino, Calif. or an operating system (e.g., Android) that is developed by Google Inc. of Mountain View, Calif.

Some embodiments of the present invention may be described in the general context of computing system executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that performs particular tasks or implement particular abstract data types. Those skilled in the art can implement the description and/or figures herein as computer-executable instructions, which can be embodied on any form of computing machine program product discussed below.

Some embodiments of the present invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

Referring to FIG. 1, the computing system 102 may include, but are not limited to, a processing unit 120 having one or more processing cores, a system memory 130, and a system bus 121 that couples various system components including the system memory 130 to the processing unit 120. The system bus 121 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) locale bus, and Peripheral Component Interconnect (PCI) bus also known as Mezzanine bus.

The computing system 102 typically includes a variety of computer program product. Computer program product can be any available media that can be accessed by computing system 102 and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer program product may store information such as computer readable instructions, data structures, program modules or other data. Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computing system 102. Communication media typically embodies computer readable instructions, data structures, or program modules.

The system memory 130 may include computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM) 131 and random access memory (RAM) 132. A basic input/output system (BIOS) 133, containing the basic routines that help to transfer information between elements within computing system 102, such as during start-up, is typically stored in ROM 131. RAM 132 typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit 120. By way of example, and not limitation, FIG. 1 also illustrates operating system 134, application programs 135, other program modules 136, and program data 137.

The computing system 102 may also include other removable/non-removable volatile/nonvolatile computer storage media. By way of example only, FIG. 1 also illustrates a hard disk drive 141 that reads from or writes to non-removable, nonvolatile magnetic media, a magnetic disk drive 151 that reads from or writes to a removable, nonvolatile magnetic disk 152, and an optical disk drive 155 that reads from or writes to a removable, nonvolatile optical disk 156 such as, for example, a CD ROM or other optical media. Other removable/non-removable, volatile/nonvolatile computer storage media that can be used in the exemplary operating environment include, but are not limited to, USB drives and devices, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, and the like. The hard disk drive 141 is typically connected to the system bus 121 through a non-removable memory interface such as interface 140, and magnetic disk drive 151 and optical disk drive 155 are typically connected to the system bus 121 by a removable memory interface, such as interface 150.

The drives and their associated computer storage media discussed above and illustrated in FIG. 1, provide storage of computer readable instructions, data structures, program modules and other data for the computing system 102. In FIG. 1, for example, hard disk drive 141 is illustrated as storing operating system 144, application programs 145, other program modules 146, and program data 147. Note that these components can either be the same as or different from operating system 134, application programs 135, other program modules 136, and program data 137. The operating system 144, the application programs 145, the other program modules 146, and the program data 147 are given different numeric identification here to illustrate that, at a minimum, they are different copies.

A user may enter commands and information into the computing system 102 through input devices such as a keyboard 162, a microphone 163, and a pointing device 161, such as a mouse, trackball or touch pad or touch screen. Other input devices (not shown) may include a joystick, game pad, scanner, or the like. These and other input devices are often connected to the processing unit 120 through a user input interface 160 that is coupled with the system bus 121, but may be connected by other interface and bus structures, such as a parallel port, game port or a universal serial bus (USB). A monitor 191 or other type of display device is also connected to the system bus 121 via an interface, such as a video interface 190. In addition to the monitor, computers may also include other peripheral output devices such as speakers 197 and printer 196, which may be connected through an output peripheral interface 190.

The computing system 102 may operate in a networked environment using logical connections to one or more remote computers, such as a remote computer 180. The remote computer 180 may be a personal computer, a hand-held device, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computing system 102. The logical connections depicted in FIG. 1 includes a local area network (LAN) 171 and a wide area network (WAN) 173, but may also include other networks. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet.

When used in a LAN networking environment, the computing system 102 may be connected to the LAN 171 through a network interface or adapter 170. When used in a WAN networking environment, the computing system 102 typically includes a modem 172 or other means for establishing communications over the WAN 173, such as the Internet. The modem 172, which may be internal or external, may be connected to the system bus 121 via the user-input interface 160, or other appropriate mechanism. In a networked environment, program modules depicted relative to the computing system 102, or portions thereof, may be stored in a remote memory storage device. By way of example, and not limitation, FIG. 1 illustrates remote application programs 185 as residing on remote computer 180. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used.

It should be noted that some embodiments of the present invention may be carried out on a computing system such as that described with respect to FIG. 1. However, some embodiments of the present invention may be carried out on a server, a computer devoted to message handling, handheld devices, or on a distributed system in which different portions of the present design may be carried out on different parts of the distributed computing system.

Another device that may be coupled with the system bus 121 is a power supply such as a battery or a Direct Current (DC) power supply) and Alternating Current (AC) adapter circuit. The DC power supply may be a battery, a fuel cell, or similar DC power source needs to be recharged on a periodic basis. The communication module (or modem) 172 may employ a Wireless Application Protocol (WAP) to establish a wireless communication channel. The communication module 172 may implement a wireless networking standard such as Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard, IEEE std. 802.11-1999, published by IEEE in 1999.

Examples of mobile computing systems may be a laptop computer, a tablet computer, a Netbook, a smart phone, a personal digital assistant, or other similar device with on board processing power and wireless communications ability that is powered by a Direct Current (DC) power source that supplies DC voltage to the mobile computing system and that is solely within the mobile computing system and needs to be recharged on a periodic basis, such as a fuel cell or a battery.

FIG. 2 shows a diagram of an example network environment that may be used with some embodiments of the present invention. Network environment 200 includes computing systems 290 and 291. One or more of the computing systems 290 and 291 may be a mobile computing system. The computing systems 290 and 291 may be connected to the network 250 via a cellular connection or via a Wi-Fi router (not shown). The network 250 may be the Internet. The computing systems 290 and 291 may be coupled with server computing systems 255 and 265 via the network 250.

Each of the computing systems 290 and 291 may include an application module such as module 208 or 214. For example, a user (e.g., a developer) may use the computing system 290 and the application module 208 to connect to and communicate with the server computing system 255 and log into application 257 (e.g., a Salesforce.com® application). For some embodiments, the user may use the application 257 to set up a dataflow life cycle for executing a dataflow and one or more of pre-processing and post-processing customized codes. The pre-processing and post-processing customized codes may be executed by the server computing system 255. The dataflow may be executed by the server computing system 265 associated with the server computing system 255. For some embodiments, the server computing system 265 may be configured to execute only the dataflow and not the pre-processing customized codes or the post-processing customized code. The server 265 may be associated with database 112. For example, the server computing system 265 may be configured to perform processing service implemented using the Superpod product of Salesforce.com. The processing service may include executing one or more dataflows using information stored in the database 112.

FIGS. 3A and 3B shows example diagrams of dataflow life cycles, in accordance with some embodiments. One or more of the pre-processing customized codes 305 and post-processing customized codes 310 may be connected to the dataflow 106 using connectors, forming a dataflow life cycle 301. A dataflow may include a set of instructions that specifies what data to extract from objects, load the data, index the data, and make the data available for analysis or additional transformations. For some embodiments, a dataflow may be versioned set of data transformation instructions to be sent to the processing service of server computing system 265. It may be possible for a dataflow life cycle to have just the dataflow with no pre-processing or post-processing customized codes, just the pre-processing customized code, just the post-processing customized code, or both pre-processing and post-processing customized codes. Following is an example of a procedure that a developer may use to connect the pre-processing and post processing customized codes to a dataflow using Wave Analytic (also referred to as Analytics Cloud) which is a business intelligence (BI) platform from Salesforce.com that is optimized for mobile access and data visualization.

-   -   1. Create a new WaveDataConnectorType. (e.g., myConnectorType)     -   2. Create a new Java connector class. (e.g., myConnector.java)     -   3. Create a mapping in WaveConnectorFactory to point the         WaveDataConnectorType to the right Java connector class. (e.g.,         point myConnectorType to myConnector.java class)     -   4. Create a DataflowLifeCycle object to connect the dataflow         with the connector. (using a DataflowLifeCycle api)     -   5. Override PrepareData to add customized pre-processing java         code. The framework will call prepareData( ) The developer can         generate call onDataReady( ) either sync or async.     -   6. Override PostExecute to add customized post-processing Java         code     -   7. Override generateWorkflow to customize payload generation         (optional step)     -   8. Override onDataReady (optional step)     -   9. Provide team info and point of contact. If any custom code         failed, an error may be generated using the team information and         contact information.         The code executed in step 5 (pre-processing Java code) of the         above example procedure may include the following operations:     -   1. Create a dataflow request in a pre-processing state     -   2. Execute pre-processing customized Java code.     -   3. If an error or exception occurs, raise error condition and         notify the developer.     -   4. On ready, change the dataflow request from the pre-processing         state to the dataflow state.         The code executed in step 6 (post-processing Java code) of the         above example procedure may include the following operations:     -   1. Change dataflow request from the dataflow state to a         post-processing state     -   2. Send message to execute post-processing customized Java code.     -   3. If an error or exception occurs, raise error condition and         notify the developer.     -   4. If no error, change the dataflow request from the         post-processing state to the complete state.     -   5. Notify completion of dataflow life cycle.         It may be noted that although the above example procedure shows         the pre-processing and post-processing customized codes written         using Java programming language, other programming languages may         also be used.

For some embodiments, the execution of the dataflow 106 may be independent of the execution of the pre-processing and post-processing customized codes 305, 310. When the dataflow life cycle 301 includes one or more of the pre-processing and post-processing customized codes 305, 310, the dataflow UI may be configured to display that these customized codes are parts of the dataflow life cycle 301. The execution of the dataflow life cycle 301 may be manually started using the dataflow UI, or it may be automatically started using an application.

It may be noted that dataflows may be developed and supported by a group of developers (e.g., ELT team) to perform operations related to ELT. As such, it may be necessary to avoid breaking the operations of the dataflow because there may be hundreds of thousands of dataflows executed per day. In comparison, the pre-processing customized codes and the post-processing customized codes may be written based on requirements of customers of the ELT team and may vary depending on the customers and/or requirements.

It may be possible to connect the same dataflow to different pre-processing and post-processing customized codes to generate different dataflow life cycles. For example, the dataflow life cycle 301 shown in FIG. 3A may be a different dataflow life cycle from the data flow life cycles 302 and 303 shown in FIG. 3B. Being a different dataflow life cycle, the dataflow identification 325 of the dataflow life cycle 301 may be different from the dataflow identification 325 of the dataflow life cycles 302 and 303. For some embodiments, even when a dataflow is connected to different pre-processing and post-processing customized codes, the execution of the dataflow remains independent of the pre-processing and post-processing customized codes and may generate the same transformed dataset 115 (shown in FIG. 1A). Further, a user may cancel the execution of the dataflow 106 at any time using the dataflow UI whether the dataflow 106 is connected to the pre-processing or post-processing customized codes 305, 310.

There may be different versions of the same dataflow life cycle. For example, when the pre-processing customized code 305 or the post-processing customized codes 310 associated with the dataflow life cycle 302 is updated, the dataflow life cycle 303 may be generated. Both dataflow life cycles 302 and 303 may be associated with the same dataflow life cycle identification (ID) 325, but they may have different dataflow life cycle version 315.

For some embodiments, each dataflow life cycle may include a configuration information field 320 used to store context information. For example, the configuration information field 320 may store information about the developer that creates the dataflow, the development team that the developer belongs to, etc. For some embodiments, the configuration information field 320 may be implemented as a JavaScript Object Notation (JSON) field which is a data-interchange format that is easy for human to read and write and for computer systems to parse and generate. For example, the usage of JSON blob in SQL database may ensure the schema change of the dataflow lifecycle will not require a data migration. Because data migration is a complex process, it's desirable to avoid that process if possible. By using JSON blob and store information in JSON, no schema change may be needed even if there is a need to change the schema design. For some embodiments, since different development team may have different needs for different columns, it's possible to use a Cassandra database (an open source NoSQL database developed by Apache Software Foundation) or columnar table as an alternative to store the configuration information. The configuration information field 320 may be used to better communicate with the developer in case when there are bugs or internal errors associated with the execution of the dataflow life cycle with which the developer is associated.

For some embodiments, a dataflow life cycle may be designed to be immutable. This means a dataflow life cycle may not be changed once it is created. This may be because a change to a data flow life cycle is infrequent and it is desirable to capture all changes. Further, it may be possible that the functionalities of a data flow life cycle are not to capture the whole java class of pre/post execution. Instead, it may be to capture an enum in data flow life cycle. During runtime execution, the enum may be looked up in an application server to determine the pre-processing and post-processing customized codes that the enum is associated with. As such, if there is a change to the java code associated with the pre-processing or post-processing customized codes may not trigger a change to the data flow life cycle as long as the linking enum is not changed.

FIG. 4 shows a diagram of an example dataflow that is incorporated with pre-processing customized codes, in accordance with some embodiments. In this example, it is assumed that there exists a pre-processing customized code in the dataflow life cycle. A dataflow life cycle manager 415 may be used to manage the different dataflow life cycles, including those with different identifications and different versions. There may be one or more dataflow life cycle managers. When a dataflow life cycle is to be executed (e.g., via schedule start), the dataflow life cycle manager 415 may select a most current version of a dataflow life cycle from the multiple versions. Having multiple versions enable keeping track of a history or a log of the updates to the pre-processing or post-processing customized codes 305, 310 of a dataflow life cycle.

For some embodiments, prior to executing a dataflow life cycle, a de-duplication module 420 may be used to determine that the same dataflow life cycle version is not requested several times at the same time. The de-duplication operations may vary depending on how the execution of the dataflow life cycle is started. For a manual start, the de-duplication module 420 may return an exception to notify user that a duplicated dataflow request already exists, then it aborts the request creation. For a schedule start, the de-duplication module 420 may create a dataflow request in a cancelled state so that it can be shown in the execution status. The de-duplication module 420 is shown to be a separate module from the pre-processing customized codes 310 in order to enable determining potential existence of duplicated requests. For some embodiments, when a start is based on execution plan (EP), de-duplication operation may not be necessary.

Based on the data flow life cycle being the only one requested, the dataflow life cycle may be placed in a pre-processing state, and the execution of the pre-processing customized codes 305 may start. In the pre-processing state, data may be processed by another computer system instead of the application server 255. If an error occurs during the execution of the pre-processing code, an error or a gack 405 may be generated, and the execution of the data flow life cycle may be stopped. Notification of the error during the pre-processing execution may be sent to a developer associated with the dataflow life cycle. If there is no error during the execution of the pre-processing code, the execution of the dataflow life cycle may continue with the dataflow itself. It may be noted that after the execution of the pre-processing code is completed, the dataflow life cycle may resume processing and may change the actual dataflow j son.

FIG. 5 shows a diagram of an example dataflow that is incorporated with post-processing customized codes, in accordance with some embodiments. In this example, it is assumed that there exists a post-processing customized code in the dataflow life cycle. After the successful execution of the dataflow, the execution of the post-processing customized code 310 may follow. Since the execution of the dataflow is independent of the post-processing customized code 310, the dataflow status 515 may be updated. Following is an example code that may be used to cause execution of the post-processing customized code. This code updates the state of the dataflowInstance as post processing state, and then send an MQ messsage to run postExecution instead of sendNotification.

  MQ handler: Try { Customized Java Code( ) } catch(Throwable allException) { gack(The team info, point of contact, gack level); } Mark dataflowInstance completed sendNotification( );

If there is an error during the execution of the post-processing customized code 310, a post-processing geck or error 510 may be generated, and the appropriate developer may be notified. For some embodiments, an error that occurs during the execution of the post-processing customized codes may be considered an error that occurs to the entire dataflow life cycle. For some other embodiments, an error that occurs during the execution of the post-processing customized codes may be considered separately from the status of the execution of the dataflow. Thus it may be possible to have an error for the post-processing and no error for the dataflow. Errors associated with the pre-processing or post-processing customized codes may be forwarded to the developers associated with the pre-processing and post-processing customized codes. Errors associated with the dataflow may be forwarded to the developer associated with the dataflow.

It may be noted that the execution of the pre-processing and post-processing customized codes 305, 310 may not affect any queue limit associated with the scheduler 107 because they are not considered a part of a dataflow (or running) state. It may also be noted that the execution of a dataflow life cycle can be stopped or cancelled at any time regardless of the state that the dataflow cycle is in. For some embodiments, a deadlock situation may occur when the execution of the pre-processing or post-processing customized codes takes longer than a predetermined time limit. When there is a deadlock situation, the affected dataflow life cycle may be terminated, a gack may be generated, and the associated developers may be notified.

For some embodiments, the pre-processing code and the post-processing code may be generated by developers, while the dataflow may be generated by end users. In some situations, the dataflow may also be generated by the developers. It may be noted from FIGS. 4 and 5 that the execution of the dataflow exists in both examples. In comparison, the execution of the pre-processing customized code and the post-processing customized code may be optional and may vary depending on the needs of the customers associated with the dataflows. The execution of the data flow may be different from the execution of the pre-processing customized code and the post-processing customized code because of the business functionalities or logics associated with the dataflow. Further, the execution of the dataflow in the examples of FIGS. 4 and 5 to be performed by a different computing system (e.g., processing service/server 265) from the exeution of the pre-processing and post-processing customized codes (e.g., application server 255). For some embodiments, if any of the pre-processing and post-processing customized codes can be executed by the same computing system as the computing system that executes the dataflow, then it may be possible to incorporate that customized code into the dataflow itself.

Following is an example of an execution of a dataflow life cycle that includes pre-processing customized codes, a dataflow, and post-processing customized codes. In this example, the dataflow 106 may include json code configured to perform operations such as “use data generated by an artificial intelligence (AI) system and save it to dataset.” In general, the AI system may be invoked as part of the pre-processing customized code 305 and generates some handles on the AI data. The dataflow 106 may then execute by collecting the AI data using the handles and transforming the AI data into a dataset. After completion of the execution of the dataflow 106, notifications may be generated as part of the post-processing customized code 310. Further, pre-processing may include one or more of execution of some Java code, sending data to an external system/database like HBase to do some data massage before dataflow execution, etc. The dataflow 106 may consist of the following instructions:

-   -   1. Load accounts: collect all accounts information from a         database.     -   2. Load AI data: read some comma-separated values (CSV) file         with generated AI data.     -   3. Join-accounts-and-AI: join results of previous operations.     -   4. Export-to-datapool: Save results to Hbase.         The operations of the dataflow life cycle may include:     -   1. The scheduler 107 may retrieve the dataflow to be executed.         The scheduler 107 may ask all known dataflow life cycle managers         if one is able to handle the execution of the dataflow 106. For         some embodiments, the scheduler 107 performs the function of         identifying a dataflow that belongs to a dataflow life cycle.     -   2. One of the dataflow life cycle managers may accept the         request to handle the execution of the dataflow 106.     -   3. The scheduler 107 may provide the dataflow j son code of the         dataflow 107 to the dataflow life cycle manager 415 that is         available to handle the execution of the dataflow 107.     -   4. The dataflow life cycle manager 415 may send a request to         generate AI data asynchronously. For example, the request may be         sent to an AI processing system (not shown) other than the         application server 255 and the processing service 265 (shown in         FIGS. 4 and 5). At this point, the dataflow life cycle manager         may be done with the pre-processing codes.     -   5. The AI processing system may finish the AI processing and         saves the CSV with some identification (e.g., csv12345).     -   6. The AI processing system may notify the dataflow life cycle         manager 415 that the CSV is ready and may request for the         dataflow life cycle to resume processing.     -   7. The dataflow life cycle manager uses the csv identification         (csv12345) to generate the following substitute dataflow json:         -   load-accounts         -   load-AI-data-csv12345         -   digest-CSV         -   join-accounts-and-AI         -   generate-CSV         -   save-CSV-to-Hbase     -   8. The updated dataflow may then be sent to the processing         service 265 (e.g., the Superpod) for processing. At this point,         the dataflow lifecycle manager may be done with sending.     -   9. The processing service 265 (e.g., the Superpod) may inform         the scheduler 107 that the execution of the dataflow 106 is         complete. The scheduler 107 may identify the dataflow lifecycle         manager 415 as the handler of the dataflow 106 and notify the         dataflow life cycle manager 415.     -   10. The dataflow life cycle manager 415 may then invoke the         execution of the post-processing customized code. Appropriate         notifications may then be issued.

FIG. 6 is an example flow chart that shows a process of executing pre-processing customized codes prior to executing a dataflow, in accordance with some embodiments. Although not shown, the process 600 may include operations to define a connector, a class, and connecting a pre-processing customized code to a dataflow, as described with FIGS. 3A and 3B. The process 600 may start at block 605. At block 610, a test may be performed to determine whether a dataflow life cycle includes a pre-processing customized code. If there exists a pre-processing customized code 305, a pre-processing state may be set, and the process may flow to block 615 where the pre-processing customized code 305 may be executed. For some embodiments, execution of the pre-processing customized code 305 may occur in the application server 255 (as shown in FIG. 4). At block 620, a test may be performed to determine if there is any error in executing the pre-processing customized code 305. If there is an error, the process may flow from block 620 to block 635 where an error notification may be generated and sent to the developer associated with the pre-processing customized code 305. From there, the process 600 may stop at block 750.

From block 620, if there is no error in the execution of the pre-processing code 305, the process may flow to block 625 where a successful notification may be generated and sent to the developer associated with the pre-processing customized code 305. From block 610, if there is no pre-processing customized code, the process 600 may continue to block 630.

At block 630, a dataflow state may be set, and the process may continue to block 630 where the execution of the dataflow 106 may occur. For some embodiments, execution of the dataflow 106 may occur in the processing service 265 (as shown in FIG. 4). At block 645, a test may be performed to determine if there is an error during the execution of the dataflow 106. If there is an error, a dataflow execution status may be updated to indicate the error. The dataflow execution status may be monitored using the dataflow UI. From there, the process 600 may stop at block 750. From block 645, if there is no error, the process 600 may continue to block 705 of FIG. 7.

FIG. 7 is an example flowchart that shows execution of post-processing customized codes after execution of a dataflow, in accordance with some embodiments. The process 700 may include post-processing operations that are only performed after a dataflow has been successfully executed. The process 700 may start at block 705. At block 710, the dataflow execution status may be updated to indicate that the dataflow has been successfully executed. At block 715, a test may be performed to determine if the post-processing customized code 310 exists in the dataflow life cycle. If the post-processing customized code 310 exists, the process 700 may flow to block 720 where the execution of the post-processing customized code may start. For some embodiments, execution of the post-processing customized code 310 may occur in the application server 255 (as shown in FIG. 5).

At block 725, a test may be performed to determine if there is any error in the execution of the post-processing customized codes 310. If there is an error, the process 700 may continue to block 730 where an error notification may be generated and forwarded to the developer of the post-processing customized code 310. If there is no error, the process 700 may continue to block 735 where a successful notification may be generated. From block 735 or 735, the process 700 may stop at block 750. From block 715, if there is no post-processing customized code, the process may stop at block 750.

FIG. 8A shows a system diagram 800 illustrating architectural components of an on-demand service environment, in accordance with some embodiments. A client machine located in the cloud 804 (or Internet) may communicate with the on-demand service environment via one or more edge routers 808 and 812. The edge routers may communicate with one or more core switches 820 and 824 via firewall 816. The core switches may communicate with a load balancer 828, which may distribute server load over different pods, such as the pods 840 and 844. The pods 840 and 844, which may each include one or more servers and/or other computing resources, may perform data processing and other operations used to provide on-demand Services. Communication with the pods may be conducted via pod switches 832 and 836. Components of the on-demand service environment may communicate with a database storage system 856 via a database firewall 848 and a database switch 852.

As shown in FIGS. 8A and 8B, accessing an on-demand service environment may involve communications transmitted among a variety of different hardware and/or software components. Further, the on-demand service environment 800 is a simplified representation of an actual on-demand service environment. For example, while only one or two devices of each type are shown in FIGS. 8A and 8B, some embodiments of an on-demand service environment may include anywhere from one to many devices of each type. Also, the on-demand service environment need not include each device shown in FIGS. 8A and 8B, or may include additional devices not shown in FIGS. 8A and 8B.

Moreover, one or more of the devices in the on-demand service environment 800 may be implemented on the same physical device or on different hardware. Some devices may be implemented using hardware or a combination of hardware and software. Thus, terms such as “data processing apparatus,” “machine,” “server” and “device” as used herein are not limited to a single hardware device, but rather include any hardware and software configured to provide the described functionality.

The cloud 804 is intended to refer to a data network or plurality of data networks, often including the Internet. Client machines located in the cloud 804 may communicate with the on-demand service environment to access services provided by the on-demand service environment. For example, client machines may access the on-demand service environment to retrieve, store, edit, and/or process information.

In some embodiments, the edge routers 808 and 812 route packets between the cloud 804 and other components of the on-demand service environment 800. The edge routers 808 and 812 may employ the Border Gateway Protocol (BGP). The BGP is the core routing protocol of the Internet. The edge routers 808 and 812 may maintain a table of IP networks or ‘prefixes’ which designate network reachability among autonomous systems on the Internet.

In one or more embodiments, the firewall 816 may protect the inner components of the on-demand service environment 800 from Internet traffic. The firewall 816 may block, permit, or deny access to the inner components of the on-demand service environment 800 based upon a set of rules and other criteria. The firewall 816 may act as one or more of a packet filter, an application gateway, a stateful filter, a proxy server, or any other type of firewall.

In some embodiments, the core switches 820 and 824 are high-capacity switches that transfer packets within the on-demand service environment 800. The core switches 820 and 824 may be configured as network bridges that quickly route data between different components within the on-demand service environment. In some embodiments, the use of two or more core switches 820 and 824 may provide redundancy and/or reduced latency.

In some embodiments, the pods 840 and 844 may perform the core data processing and service functions provided by the on-demand service environment. Each pod may include various types of hardware and/or software computing resources. An example of the pod architecture is discussed in greater detail with reference to FIG. 8B.

In some embodiments, communication between the pods 840 and 844 may be conducted via the pod switches 832 and 836. The pod switches 832 and 836 may facilitate communication between the pods 840 and 844 and client machines located in the cloud 804, for example via core switches 820 and 824. Also, the pod switches 832 and 836 may facilitate communication between the pods 840 and 844 and the database storage 856.

In some embodiments, the load balancer 828 may distribute workload between the pods 840 and 844. Balancing the on-demand service requests between the pods may assist in improving the use of resources, increasing throughput, reducing response times, and/or reducing overhead. The load balancer 828 may include multilayer switches to analyze and forward traffic.

In some embodiments, access to the database storage 856 may be guarded by a database firewall 848. The database firewall 848 may act as a computer application firewall operating at the database application layer of a protocol stack. The database firewall 848 may protect the database storage 856 from application attacks such as structure query language (SQL) injection, database rootkits, and unauthorized information disclosure.

In some embodiments, the database firewall 848 may include a host using one or more forms of reverse proxy services to proxy traffic before passing it to a gateway router. The database firewall 848 may inspect the contents of database traffic and block certain content or database requests. The database firewall 848 may work on the SQL application level atop the TCP/IP stack, managing applications' connection to the database or SQL management interfaces as well as intercepting and enforcing packets traveling to or from a database network or application interface.

In some embodiments, communication with the database storage system 856 may be conducted via the database switch 852. The multi-tenant database system 856 may include more than one hardware and/or software components for handling database queries. Accordingly, the database switch 852 may direct database queries transmitted by other components of the on-demand service environment (e.g., the pods 840 and 844) to the correct components within the database storage system 856. In some embodiments, the database storage system 856 is an on-demand database system shared by many different organizations. The on-demand database system may employ a multi-tenant approach, a virtualized approach, or any other type of database approach. An on-demand database system is discussed in greater detail with reference to FIGS. 9 and 10.

FIG. 8B shows a system diagram illustrating the architecture of the pod 844, in accordance with one embodiment. The pod 844 may be used to render services to a user of the on-demand service environment 800. In some embodiments, each pod may include a variety of servers and/or other systems. The pod 844 includes one or more content batch servers 864, content search servers 868, query servers 872, file force servers 876, access control system (ACS) servers 880, batch servers 884, and app servers 888. Also, the pod 844 includes database instances 890, quick file systems (QFS) 892, and indexers 894. In one or more embodiments, some or all communication between the servers in the pod 844 may be transmitted via the switch 836.

In some embodiments, the application servers 888 may include a hardware and/or software framework dedicated to the execution of procedures (e.g., programs, routines, scripts) for supporting the construction of applications provided by the on-demand service environment 800 via the pod 844. Some such procedures may include operations for providing the services described herein. The content batch servers 864 may request internal to the pod. These requests may be long-running and/or not tied to a particular customer. For example, the content batch servers 864 may handle requests related to log mining, cleanup work, and maintenance tasks.

The content search servers 868 may provide query and indexer functions. For example, the functions provided by the content search servers 868 may allow users to search through content stored in the on-demand service environment. The Fileforce servers 876 may manage requests information stored in the Fileforce storage 878. The Fileforce storage 878 may store information such as documents, images, and basic large objects (BLOBs). By managing requests for information using the Fileforce servers 876, the image footprint on the database may be reduced.

The query servers 872 may be used to retrieve information from one or more file systems. For example, the query system 872 may receive requests for information from the app servers 888 and then transmit information queries to the NFS 896 located outside the pod. The pod 844 may share a database instance 890 configured as a multi-tenant environment in which different organizations share access to the same database. Additionally, services rendered by the pod 844 may require various hardware and/or software resources. In some embodiments, the ACS servers 880 may control access to data, hardware resources, or software resources.

In some embodiments, the batch servers 884 may process batch jobs, which are used to run tasks at specified times. Thus, the batch servers 884 may transmit instructions to other servers, such as the app servers 888, to trigger the batch jobs. For some embodiments, the QFS 892 may be an open source file system available from Sun Microsystems® of Santa Clara, Calif. The QFS may serve as a rapid-access file system for storing and accessing information available within the pod 844. The QFS 892 may support some volume management capabilities, allowing many disks to be grouped together into a file system. File system metadata can be kept on a separate set of disks, which may be useful for streaming applications where long disk seeks cannot be tolerated. Thus, the QFS system may communicate with one or more content search servers 868 and/or indexers 894 to identify, retrieve, move, and/or update data stored in the network file systems 896 and/or other storage systems.

In some embodiments, one or more query servers 872 may communicate with the NFS 896 to retrieve and/or update information stored outside of the pod 844. The NFS 896 may allow servers located in the pod 844 to access information to access files over a network in a manner similar to how local storage is accessed. In some embodiments, queries from the query servers 822 may be transmitted to the NFS 896 via the load balancer 820, which may distribute resource requests over various resources available in the on-demand service environment. The NFS 896 may also communicate with the QFS 892 to update the information stored on the NFS 896 and/or to provide information to the QFS 892 for use by servers located within the pod 844.

In some embodiments, the pod may include one or more database instances 890. The database instance 890 may transmit information to the QFS 892. When information is transmitted to the QFS, it may be available for use by servers within the pod 844 without requiring an additional database call. In some embodiments, database information may be transmitted to the indexer 894. Indexer 894 may provide an index of information available in the database 890 and/or QFS 892. The index information may be provided to file force servers 876 and/or the QFS 892.

FIG. 9 shows a block diagram of an environment 910 wherein an on-demand database service might be used, in accordance with some embodiments. Environment 910 includes an on-demand database service 916. User system 912 may be any machine or system that is used by a user to access a database user system. For example, any of user systems 912 can be a handheld computing system, a mobile phone, a laptop computer, a work station, and/or a network of computing systems. As illustrated in FIGS. 9 and 10, user systems 912 might interact via a network 914 with the on-demand database service 916.

An on-demand database service, such as system 916, is a database system that is made available to outside users that do not need to necessarily be concerned with building and/or maintaining the database system, but instead may be available for their use when the users need the database system (e.g., on the demand of the users). Some on-demand database services may store information from one or more tenants stored into tables of a common database image to form a multi-tenant database system (MTS). Accordingly, “on-demand database service 916” and “system 916” will be used interchangeably herein. A database image may include one or more database objects. A relational database management system (RDBMS) or the equivalent may execute storage and retrieval of information against the database object(s). Application platform 918 may be a framework that allows the applications of system 916 to run, such as the hardware and/or software, e.g., the operating system. In an implementation, on-demand database service 916 may include an application platform 918 that enables creation, managing and executing one or more applications developed by the provider of the on-demand database service, users accessing the on-demand database service via user systems 912, or third party application developers accessing the on-demand database service via user systems 912.

One arrangement for elements of system 916 is shown in FIG. 9, including a network interface 920, application platform 918, tenant data storage 922 for tenant data 923, system data storage 924 for system data 925 accessible to system 916 and possibly multiple tenants, program code 926 for implementing various functions of system 916, and a process space 928 for executing MTS system processes and tenant-specific processes, such as running applications as part of an application hosting service. Additional processes that may execute on system 916 include database indexing processes.

The users of user systems 912 may differ in their respective capacities, and the capacity of a particular user system 912 might be entirely determined by permissions (permission levels) for the current user. For example, where a call center agent is using a particular user system 912 to interact with system 916, the user system 912 has the capacities allotted to that call center agent. However, while an administrator is using that user system to interact with system 916, that user system has the capacities allotted to that administrator. In systems with a hierarchical role model, users at one permission level may have access to applications, data, and database information accessible by a lower permission level user, but may not have access to certain applications, database information, and data accessible by a user at a higher permission level. Thus, different users may have different capabilities with regard to accessing and modifying application and database information, depending on a user's security or permission level.

Network 914 is any network or combination of networks of devices that communicate with one another. For example, network 914 can be any one or any combination of a LAN (local area network), WAN (wide area network), telephone network, wireless network, point-to-point network, star network, token ring network, hub network, or other appropriate configuration. As the most common type of computer network in current use is a TCP/IP (Transfer Control Protocol and Internet Protocol) network (e.g., the Internet), that network will be used in many of the examples herein. However, it should be understood that the networks used in some embodiments are not so limited, although TCP/IP is a frequently implemented protocol.

User systems 912 might communicate with system 916 using TCP/IP and, at a higher network level, use other common Internet protocols to communicate, such as HTTP, FTP, AFS, WAP, etc. In an example where HTTP is used, user system 912 might include an HTTP client commonly referred to as a “browser” for sending and receiving HTTP messages to and from an HTTP server at system 916. Such an HTTP server might be implemented as the sole network interface between system 916 and network 914, but other techniques might be used as well or instead. In some embodiments, the interface between system 916 and network 914 includes load sharing functionality, such as round-robin HTTP request distributors to balance loads and distribute incoming HTTP requests evenly over a plurality of servers. At least as for the users that are accessing that server, each of the plurality of servers has access to the MTS' data; however, other alternative configurations may be used instead.

In some embodiments, system 916, shown in FIG. 9, implements a web-based customer relationship management (CRM) system. For example, in some embodiments, system 916 includes application servers configured to implement and execute CRM software applications as well as provide related data, code, forms, web pages and other information to and from user systems 912 and to store to, and retrieve from, a database system related data, objects, and Webpage content. With a multi-tenant system, data for multiple tenants may be stored in the same physical database object, however, tenant data typically is arranged so that data of one tenant is kept logically separate from that of other tenants so that one tenant does not have access to another tenant's data, unless such data is expressly shared. In certain embodiments, system 916 implements applications other than, or in addition to, a CRM application. For example, system 916 may provide tenant access to multiple hosted (standard and custom) applications. User (or third party developer) applications, which may or may not include CRM, may be supported by the application platform 918, which manages creation, storage of the applications into one or more database objects and executing of the applications in a virtual machine in the process space of the system 916.

Each user system 912 could include a desktop personal computer, workstation, laptop, PDA, cell phone, or any wireless access protocol (WAP) enabled device or any other computing system capable of interfacing directly or indirectly to the Internet or other network connection. User system 912 typically runs an HTTP client, e.g., a browsing program, such as Microsoft's Internet Explorer® browser, Mozilla's Firefox® browser, Opera's browser, or a WAP-enabled browser in the case of a cell phone, PDA or other wireless device, or the like, allowing a user (e.g., subscriber of the multi-tenant database system) of user system 912 to access, process and view information, pages and applications available to it from system 916 over network 914.

Each user system 912 also typically includes one or more user interface devices, such as a keyboard, a mouse, trackball, touch pad, touch screen, pen or the like, for interacting with a graphical user interface (GUI) provided by the browser on a display (e.g., a monitor screen, LCD display, etc.) in conjunction with pages, forms, applications and other information provided by system 916 or other systems or servers. For example, the user interface device can be used to access data and applications hosted by system 916, and to perform searches on stored data, and otherwise allow a user to interact with various GUI pages that may be presented to a user. As discussed above, embodiments are suitable for use with the Internet, which refers to a specific global internetwork of networks. However, it should be understood that other networks can be used instead of the Internet, such as an intranet, an extranet, a virtual private network (VPN), a non-TCP/IP based network, any LAN or WAN or the like.

According to some embodiments, each user system 912 and all of its components are operator configurable using applications, such as a browser, including computer code run using a central processing unit such as an Intel Pentium® processor or the like. Similarly, system 916 (and additional instances of an MTS, where more than one is present) and all of their components might be operator configurable using application(s) including computer code to run using a central processing unit such as processor system 917, which may include an Intel Pentium® processor or the like, and/or multiple processor units.

A computer program product implementation includes a machine-readable storage medium (media) having instructions stored thereon/in which can be used to program a computer to perform any of the processes of the embodiments described herein. Computer code for operating and configuring system 916 to intercommunicate and to process web pages, applications and other data and media content as described herein are preferably downloaded and stored on a hard disk, but the entire program code, or portions thereof, may also be stored in any other volatile or non-volatile memory medium or device, such as a ROM or RAM, or provided on any media capable of storing program code, such as any type of rotating media including floppy disks, optical discs, digital versatile disk (DVD), compact disk (CD), microdrive, and magneto-optical disks, and magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of media or device suitable for storing instructions and/or data. Additionally, the entire program code, or portions thereof, may be transmitted and downloaded from a software source over a transmission medium, e.g., over the Internet, or from another server, or transmitted over any other conventional network connection (e.g., extranet, VPN, LAN, etc.) using any communication medium and protocols (e.g., TCP/IP, HTTP, HTTPS, Ethernet, etc.). It will also be appreciated that computer code for implementing embodiments can be implemented in any programming language that can be executed on a client system and/or server or server system such as, for example, C, C++, HTML, any other markup language, Java™, JavaScript®, ActiveX®, any other scripting language, such as VBScript, and many other programming languages as are well known may be used. (Java™ is a trademark of Sun Microsystems®, Inc.).

According to some embodiments, each system 916 is configured to provide web pages, forms, applications, data and media content to user (client) systems 912 to support the access by user systems 912 as tenants of system 916. As such, system 916 provides security mechanisms to keep each tenant's data separate unless the data is shared. If more than one MTS is used, they may be located in close proximity to one another (e.g., in a server farm located in a single building or campus), or they may be distributed at locations remote from one another (e.g., one or more servers located in city A and one or more servers located in city B). As used herein, each MTS could include logically and/or physically connected servers distributed locally or across one or more geographic locations. Additionally, the term “server” is meant to include a computing system, including processing hardware and process space(s), and an associated storage system and database application (e.g., OODBMS or RDBMS) as is well known in the art.

It should also be understood that “server system” and “server” are often used interchangeably herein. Similarly, the database object described herein can be implemented as single databases, a distributed database, a collection of distributed databases, a database with redundant online or offline backups or other redundancies, etc., and might include a distributed database or storage network and associated processing intelligence.

FIG. 10 also shows a block diagram of environment 910 further illustrating system 916 and various interconnections, in accordance with some embodiments. FIG. 10 shows that user system 912 may include processor system 912A, memory system 912B, input system 912C, and output system 912D. FIG. 10 shows network 914 and system 916. FIG. 10 also shows that system 916 may include tenant data storage 922, tenant data 923, system data storage 924, system data 925, User Interface (UI) 1030, Application Program Interface (API) 1032, PL/SOQL 1034, save routines 1036, application setup mechanism 1038, applications servers 10001-1000N, system process space 1002, tenant process spaces 1004, tenant management process space 1010, tenant storage area 1012, user storage 1014, and application metadata 1016. In other embodiments, environment 910 may not have the same elements as those listed above and/or may have other elements instead of, or in addition to, those listed above.

User system 912, network 914, system 916, tenant data storage 922, and system data storage 924 were discussed above in FIG. 9. Regarding user system 912, processor system 912A may be any combination of processors. Memory system 912B may be any combination of one or more memory devices, short term, and/or long term memory. Input system 912C may be any combination of input devices, such as keyboards, mice, trackballs, scanners, cameras, and/or interfaces to networks. Output system 912D may be any combination of output devices, such as monitors, printers, and/or interfaces to networks. As shown by FIG. 10, system 916 may include a network interface 920 (of FIG. 9) implemented as a set of HTTP application servers 1000, an application platform 918, tenant data storage 922, and system data storage 924. Also shown is system process space 1002, including individual tenant process spaces 1004 and a tenant management process space 1010. Each application server 1000 may be configured to tenant data storage 922 and the tenant data 923 therein, and system data storage 924 and the system data 925 therein to serve requests of user systems 912. The tenant data 923 might be divided into individual tenant storage areas 1012, which can be either a physical arrangement and/or a logical arrangement of data. Within each tenant storage area 1012, user storage 1014 and application metadata 1016 might be similarly allocated for each user. For example, a copy of a user's most recently used (MRU) items might be stored to user storage 1014. Similarly, a copy of MRU items for an entire organization that is a tenant might be stored to tenant storage area 1012. A UI 1030 provides a user interface and an API 1032 provides an application programmer interface to system 916 resident processes to users and/or developers at user systems 912. The tenant data and the system data may be stored in various databases, such as Oracle™ databases.

Application platform 918 includes an application setup mechanism 1038 that supports application developers' creation and management of applications, which may be saved as metadata into tenant data storage 922 by save routines 1036 for execution by subscribers as tenant process spaces 1004 managed by tenant management process 1010 for example. Invocations to such applications may be coded using PL/SOQL 34 that provides a programming language style interface extension to API 1032. A detailed description of some PL/SOQL language embodiments is discussed in commonly assigned U.S. Pat. No. 7,730,478, titled METHOD AND SYSTEM FOR ALLOWING ACCESS TO DEVELOPED APPLICATIONS VIA A MULTI-TENANT ON-DEMAND DATABASE SERVICE, by Craig Weissman, filed Sep. 21, 2007, which is hereby incorporated by reference in its entirety and for all purposes. Invocations to applications may be detected by system processes, which manage retrieving application metadata 1016 for the subscriber making the invocation and executing the metadata as an application in a virtual machine.

Each application server 1000 may be communicably coupled to database systems, e.g., having access to system data 925 and tenant data 923, via a different network connection. For example, one application server 10001 might be coupled via the network 914 (e.g., the Internet), another application server 1000N-1 might be coupled via a direct network link, and another application server 1000N might be coupled by yet a different network connection. Transfer Control Protocol and Internet Protocol (TCP/IP) are typical protocols for communicating between application servers 1000 and the database system. However, other transport protocols may be used to optimize the system depending on the network interconnect used.

In certain embodiments, each application server 1000 is configured to handle requests for any user associated with any organization that is a tenant. Because it is desirable to be able to add and remove application servers from the server pool at any time for any reason, there is preferably no server affinity for a user and/or organization to a specific application server 1000. In some embodiments, therefore, an interface system implementing a load balancing function (e.g., an F5 Big-IP load balancer) is communicably coupled between the application servers 1000 and the user systems 912 to distribute requests to the application servers 1000. In some embodiments, the load balancer uses a least connections algorithm to route user requests to the application servers 1000. Other examples of load balancing algorithms, such as round robin and observed response time, also can be used. For example, in certain embodiments, three consecutive requests from the same user could hit three different application servers 1000, and three requests from different users could hit the same application server 1000. In this manner, system 916 is multi-tenant, wherein system 916 handles storage of, and access to, different objects, data and applications across disparate users and organizations.

As an example of storage, one tenant might be a company that employs a sales force where each call center agent uses system 916 to manage their sales process. Thus, a user might maintain contact data, leads data, customer follow-up data, performance data, goals and progress data, etc., all applicable to that user's personal sales process (e.g., in tenant data storage 922). In an example of a MTS arrangement, since all of the data and the applications to access, view, modify, report, transmit, calculate, etc., can be maintained and accessed by a user system having nothing more than network access, the user can manage his or her sales efforts and cycles from any of many different user systems. For example, if a call center agent is visiting a customer and the customer has Internet access in their lobby, the call center agent can obtain critical updates as to that customer while waiting for the customer to arrive in the lobby.

While each user's data might be separate from other users' data regardless of the employers of each user, some data might be organization-wide data shared or accessible by a plurality of users or all of the users for a given organization that is a tenant. Thus, there might be some data structures managed by system 916 that are allocated at the tenant level while other data structures might be managed at the user level. Because an MTS might support multiple tenants including possible competitors, the MTS should have security protocols that keep data, applications, and application use separate. Also, because many tenants may opt for access to an MTS rather than maintain their own system, redundancy, up-time, and backup are additional functions that may be implemented in the MTS. In addition to user-specific data and tenant specific data, system 916 might also maintain system level data usable by multiple tenants or other data. Such system level data might include industry reports, news, postings, and the like that are sharable among tenants.

In certain embodiments, user systems 912 (which may be client machines/systems) communicate with application servers 1000 to request and update system-level and tenant-level data from system 916 that may require sending one or more queries to tenant data storage 922 and/or system data storage 924. System 916 (e.g., an application server 1000 in system 916) automatically generates one or more SQL statements (e.g., SQL queries) that are designed to access the desired information. System data storage 924 may generate query plans to access the requested data from the database.

Each database can generally be viewed as a collection of objects, such as a set of logical tables, containing data fitted into predefined categories. A “table” is one representation of a data object, and may be used herein to simplify the conceptual description of objects and custom objects according to some embodiments. It should be understood that “table” and “object” may be used interchangeably herein. Each table generally contains one or more data categories logically arranged as columns or fields in a viewable schema. Each row or record of a table contains an instance of data for each category defined by the fields. For example, a CRM database may include a table that describes a customer with fields for basic contact information such as name, address, phone number, fax number, etc. Another table might describe a purchase order, including fields for information such as customer, product, sale price, date, etc. In some multi-tenant database systems, standard entity tables might be provided for use by all tenants. For CRM database applications, such standard entities might include tables for account, contact, lead, and opportunity data, each containing pre-defined fields. It should be understood that the word “entity” may also be used interchangeably herein with “object” and “table”.

In some multi-tenant database systems, tenants may be allowed to create and store custom objects, or they may be allowed to customize standard entities or objects, for example by creating custom fields for standard objects, including custom index fields. U.S. Pat. No. 7,779,039, titled CUSTOM ENTITIES AND FIELDS IN A MULTI-TENANT DATABASE SYSTEM, by Weissman, et al., and which is hereby incorporated by reference in its entirety and for all purposes, teaches systems and methods for creating custom objects as well as customizing standard objects in a multi-tenant database system. In some embodiments, for example, all custom entity data rows are stored in a single multi-tenant physical table, which may contain multiple logical tables per organization. In some embodiments, multiple “tables” for a single customer may actually be stored in one large table and/or in the same table as the data of other customers.

These and other aspects of the disclosure may be implemented by various types of hardware, software, firmware, etc. For example, some features of the disclosure may be implemented, at least in part, by machine-program product that include program instructions, state information, etc., for performing various operations described herein. Examples of program instructions include both machine code, such as produced by a compiler, and files containing higher-level code that may be executed by the computer using an interpreter. Examples of machine-program product include, but are not limited to, magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROM disks; magneto-optical media; and hardware devices that are specially configured to store and perform program instructions, such as read-only memory devices (“ROM”) and random access memory (“RAM”).

While one or more embodiments and techniques are described with reference to an implementation in which a service cloud console is implemented in a system having an application server providing a front end for an on-demand database service capable of supporting multiple tenants, the one or more embodiments and techniques are not limited to multi-tenant databases nor deployment on application servers. Embodiments may be practiced using other database architectures, i.e., ORACLE®, DB2® by IBM and the like without departing from the scope of the embodiments claimed.

Any of the above embodiments may be used alone or together with one another in any combination. Although various embodiments may have been motivated by various deficiencies with the prior art, which may be discussed or alluded to in one or more places in the specification, the embodiments do not necessarily address any of these deficiencies. In other words, different embodiments may address different deficiencies that may be discussed in the specification. Some embodiments may only partially address some deficiencies or just one deficiency that may be discussed in the specification, and some embodiments may not address any of these deficiencies.

While various embodiments have been described herein, it should be understood that they have been presented by way of example only, and not limitation. Thus, the breadth and scope of the present application should not be limited by any of the embodiments described herein, but should be defined only in accordance with the following and later-submitted claims and their equivalents. 

What is claimed is:
 1. A method comprising: forming, by a first server computing system, a dataflow life cycle by connecting a dataflow with customized code using connectors, the dataflow being a set of data transformation instructions, the customized code including one or more of pre-processing customized code and post-processing customized code; associating, by the first server computing system, the customized code of the dataflow life cycle with context information prior to executing the dataflow life cycle, wherein the context information comprises contact information for a developer of the customized code and is stored in a configuration information field included in the dataflow life cycle; scheduling, by the first server computing system, the dataflow of the dataflow life cycle to be executed by a second server computing system when the customized code includes the pre-processing customized code that is successfully executed by the first server computing system; executing, by the first server computing system, the post-processing customized code when the customized code includes the post-processing customized code and the dataflow of the dataflow life cycle is successfully executed by the second server computing system; using, by the first server computing system, the context information to notify the developer of the error in response to detecting an error during the execution of either the pre-processing or post-processing customized code by the first server computing system; generating, by the first server computing system, a pre-processing error notification using the context information, and preventing the dataflow of the dataflow life cycle from being executed by the second server computing system in response to failed execution of the pre-processing code by the first server computing system; and generating, by the first server computing system, a pre-processing successful notification using the context information in response to successful execution of the pre-processing code by the first server computing system.
 2. The method of claim 1, further comprising: updating, by the first server computing system, a dataflow execution status to indicate successful execution of the dataflow in response to successful execution of the dataflow by the second server computing system; and updating, by the first server computing system, the dataflow execution status to indicate failed execution of the dataflow, and preventing, by the first server computing system, execution of the post-processing customized code in response to failed execution of the dataflow by the second server computing system.
 3. The method of claim 2, further comprising: generating, by the first server computing system, a post-processing error notification using the context information in response to failed execution of the post-processing customized code by the first server computing system; and generating, by the first server computing system, a post-processing successful notification using the context information or a successful execution notification of the dataflow life cycle in response to successful execution of the post-processing code by the first server computing system.
 4. The method of claim 3, further comprising: modifying, by the first server computing system, one or more of the pre-processing customized code and the post-processing customized code; and saving, by the first server computing system, the dataflow life cycle with the one or more modified pre-processing customized code and modified post-processing customized code as a different version.
 5. The method of claim 4, wherein executing the dataflow life cycle comprises executing a most current version of the dataflow life cycle.
 6. The method of claim 5, wherein execution of the dataflow by the second server computing system is independent of execution of the pre-processing customized code and post-processing customized code by the first server computing system.
 7. A system comprising: one or more processors; and a non-transitory computer readable medium storing a plurality of instructions, which when executed, cause the one or more processors of a first server computing system to: form a dataflow life cycle by connecting a dataflow with customized code using connectors, the dataflow being a set of data transformation instructions, the customized code including one or more of pre-processing customized code and post-processing customized code; associate the customized code of the dataflow life cycle with context information prior to executing the dataflow life cycle, wherein the context information comprises contact information for a developer of the customized code and is stored in a configuration information field included in the dataflow life cycle; schedule the dataflow of the dataflow life cycle to be executed by a second server computing system when the customized code includes the pre-processing customized code that is successfully executed by the first server computing system; execute the post-processing customized code when the customized code includes the post-processing customized code and the dataflow of the dataflow life cycle is successfully executed by the second server computing system; use, by the first server computing system, the context information to notify the developer of the error in response to detecting an error during the execution of either the pre-processing or post-processing customized code by the first server computing system; generate, by the first server computing system, a pre-processing error notification using the context information, and preventing the dataflow of the dataflow life cycle from being executed by the second server computing system in response to failed execution of the pre-processing code by the first server computing system; and generate, by the first server computing system, a pre-processing successful notification using the context information in response to successful execution of the pre-processing code by the first server computing system.
 8. The system of claim 7, further comprising instructions to: update a dataflow execution status to indicate successful execution of the dataflow in response to successful execution of the dataflow by the second server computing system; and update the dataflow execution status to indicate failed execution of the dataflow, and prevent execution of the post-processing customized code in response to failed execution of the dataflow by the second server computing system.
 9. The system of claim 8, further comprising instructions to: generate a post-processing error notification using the context information in response to failed execution of the post-processing customized code by the first server computing system; and generate a post-processing successful notification using the context information or a successful execution notification of the dataflow life cycle in response to successful execution of the post-processing code by the first server computing system.
 10. The system of claim 9, further comprising instructions to: modify one or more of the pre-processing customized code and the post-processing customized code; and save the dataflow life cycle with the one or more modified pre-processing customized code and modified post-processing customized code as a different version.
 11. The system of claim 10, wherein the instructions to execute the dataflow life cycle comprises instructions to execute a most current version of the dataflow life cycle.
 12. The system of claim 11, wherein the instructions to execute the dataflow by the second server computing system is independent of the instructions to execute the pre-processing customized code and post-processing customized code by the first server computing system.
 13. A computer program product comprising computer-readable program code to be executed by one or more processors of a first server computing system when retrieved from a non-transitory computer-readable medium, the program code including instructions to: form a dataflow life cycle by connecting a dataflow with customized code using connectors, the dataflow being a set of data transformation instructions, the customized code including one or more of pre-processing customized code and post-processing customized code; associate the customized code of the dataflow life cycle with context information prior to executing the dataflow life cycle, wherein the context information comprises contact information for a developer of the customized code and is stored in a configuration information field included in the dataflow life cycle; schedule the dataflow of the dataflow life cycle to be executed by a second server computing system when the customized code includes the pre-processing customized code that is successfully executed by the first server computing system; execute the post-processing customized code when the customized code includes the post-processing customized code and the dataflow of the dataflow life cycle is successfully executed by the second server computing system; use, by the first computing system, the context information to notify the developer of the error in response to detecting an error during the execution of either the pre-processing or post-processing customized code by the first server computing system; generate a pre-processing error notification using the context information, and preventing the dataflow of the dataflow life cycle from being executed by the second server computing system in response to failed execution of the pre-processing code by the first server computing system; and generate a pre-processing successful notification using the context information in response to successful execution of the pre-processing code by the first server computing system.
 14. The computer program product of claim 13, further comprising instructions to: update a dataflow execution status to indicate successful execution of the dataflow in response to successful execution of the dataflow by the second server computing system; and update the dataflow execution status to indicate failed execution of the dataflow, and prevent execution of the post-processing customized code in response to failed execution of the dataflow by the second server computing system.
 15. The computer program product of claim 14, further comprising instructions to: generate a post-processing error notification using the context information in response to failed execution of the post-processing customized code by the first server computing system; and generate a post-processing successful notification using the context information or a successful execution notification of the dataflow life cycle in response to successful execution of the post-processing code by the first server computing system.
 16. The computer program product of claim 15, further comprising instructions to: modify one or more of the pre-processing customized code and the post-processing customized code; and save the dataflow life cycle with the one or more modified pre-processing customized code and modified post-processing customized code as a different version.
 17. The computer program product of claim 16, wherein the instructions to execute the dataflow life cycle comprises instructions to execute a most current version of the dataflow life cycle.
 18. The computer program product of claim 17, wherein the instructions to execute the dataflow by the second server computing system are independent of the instructions to execute the pre-processing customized code and post-processing customized code by the first server computing system. 